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84 lines
3.9 KiB
Python
84 lines
3.9 KiB
Python
#!/usr/bin/env python3
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"""
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Oncogenic Godzilla Audit: TP53 Healthy vs Mutated
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The ultimate bio-informatic sabotage detector.
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"""
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import sys
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import numpy as np
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from pathlib import Path
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import logging
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# Add parent directory to path
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sys.path.insert(0, str(Path(__file__).parent.parent.parent))
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sys.path.insert(0, str(Path(__file__).parent.parent.parent / "4-Infrastructure"))
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sys.path.insert(0, str(Path(__file__).parent.parent.parent / "0-Core-Formalism"))
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from scripts.rgflow_blind_detector import BlindDetector
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logging.basicConfig(level=logging.ERROR)
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def run_godzilla_audit():
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# 1. TP53 Reference mRNA (Partial/Representative Segment)
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# This is a lawful, high-informativity biological sequence.
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tp53_healthy = ("ATGGAGGAGCCGCAGTCAGATCCTAGCGTCGAGCCCCCTCTGAGTCAGGAAACATTTTCAGACCTATGGAAACTACTTCCTGAAAACAACGTTCTGTCCCC"
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"CTTGCCGTCCCAAGCAATGGATGATTTGATGCTGTCCCCGGACGATATTGAACAATGGTTCACTGAAGACCCAGGTCCAGATGAAGCTCCCAGAATGCCAG"
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"AGGCTGCTCCCCGCGTGGCCCCTGCACCAGCAGCTCCTACACCGGCGGCCCCTGCACCAGCCCCCTCCTGGCCCCTGTCATCTTCTGTCCCTTCCCAGAAA"
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"ACCTACCAGGGCAGCTACGGTTTCCGTCTGGGCTTCTTGCATTCTGGGACAGCCAAGTCTGTGACTTGCACGTACTCCCCTGCCCTCAACAAGATGTTTTG"
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"CCAACTGGCCAAGACCTGCCCCGTGCAGCTGTGGGTTGATTCCACACCCCCGCCCGGCACCCGCGTCCGCGCCATGGCCATCTACAAGCAGTCACAGCACA"
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"TGACGGAGGTTGTGAGGCGCTGCCCCCACCATGAGCGCTGCTCAGATAGCGATGGTCTGGCCCCTCCTCAGCATCTTATCCGAGTGGAAGGAAATTTGCGT"
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"GTGGAGTATTTGGATGACAGAAACACTTTTCGACATAGTGTGGTGGTGCCCTATGAGCCGCCTGAGGTTGGCTCTGACTGTACCACCATCCACTACAACTA"
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"CATGTGTAACAGTTCCTGCATGGGCGGCATGAACCGGAGGCCCATCCTCACCATCATCACACTGGAAGACTCCAGTGGTAATCTACTGGGACGGAACAGCT"
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"TTGAGGTGCGTGTTTGTGCCTGTCCTGGGAGAGACCGGCGCACAGAGGAAGAGAATCTCCGCAAGAAAGGGGAGCCTCACCACGAGCTGCCCCCAGGGAGC"
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"ACTAAGCGAGCACTGCCCAACAACACCAGCTCCTCTCCCCAGCCAAAGAAGAAACCACTGGATGGAGAATATTTCACCCTTCAGATCCGTGGGCGTGAGCG"
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"CTTCGAGATGTTCCGAGAGCTGAATGAGGCCTTGGAACTCAAGGATGCCCAGGCTGGGAAGGAGCCAGGGGGGAGCAGGGCTCACTCCAGCCACCTGAAGT"
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"CCAAAAAGGGTCAGTCTACCTCCCGCCATAAAAAACTCATGTTCAAGACAGAAGGGCCTGACTCAGACTGA")
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# 2. Inject "Godzilla" Hotspot Mutations
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# R175H (Arg -> His at codon 175)
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# R248W (Arg -> Trp at codon 248)
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# These are devastating informatic collapses in the genome.
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tp53_cancer = list(tp53_healthy)
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# R175H: Typical CGC -> CAC transition
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loc_175 = 175 * 3
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tp53_cancer[loc_175:loc_175+3] = list("CAC")
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# R248W: Typical CGG -> TGG transition
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loc_248 = 248 * 3
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tp53_cancer[loc_248:loc_248+3] = list("TGG")
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tp53_cancer = "".join(tp53_cancer)
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# 3. RGFlow Differential Audit
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detector = BlindDetector()
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print("--- ONCOGENIC GODZILLA DIFFERENTIAL AUDIT: TP53 ---")
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hotspots = [loc_175, loc_248]
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for start in hotspots:
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window_h = tp53_healthy[max(0, start-100) : min(len(tp53_healthy), start+100)]
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window_c = tp53_cancer[max(0, start-100) : min(len(tp53_cancer), start+100)]
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state_h = detector.calculate_window_state(window_h)
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state_c = detector.calculate_window_state(window_c)
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# Calculate Delta Sigma
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# In cancer, the mutation drops the spectral coherence of the codon block
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delta_sigma = state_h.sigma_q - state_c.sigma_q
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print(f"\nLocus {start} (Codon {start//3}):")
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print(f" Healthy Sigma: {state_h.sigma_q:.6f}")
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print(f" Cancer Sigma: {state_c.sigma_q:.6f}")
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if state_c.sigma_q < state_h.sigma_q:
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loss = (delta_sigma / state_h.sigma_q) * 100
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print(f" [!] DETECTED: Informatic Collapse ({loss:.2f}% reduction in scale-stability)")
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print(f" [+] RECOMMENDATION: Informatic Stripping (Restoration to reference)")
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else:
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print(f" Match: Scale-stability preserved or neutral.")
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print("\n--- AUDIT COMPLETE ---")
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if __name__ == "__main__":
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run_godzilla_audit()
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